Transforming financial reporting and auditing through artificial intelligence: A Zimbabwean institutional perspective - Munyepwa, Kudakwashe; Ranganayi, Charity; Mudzengerere, Liberty; Mutongereni, Noah; Gwesu, Norah Chishamiso (International Journal of Financial, Accounting, and Management, 2025)
Purpose: This study investigates the potential of Artificial Intelligence (AI) to enhance financial reporting and auditing practices in Zimbabwean institutions amid economic volatility and increasing transparency demands. Research Methodology: The primary aim is to evaluate how AI-driven tools can improve the accuracy and reliability of financial information in both public and private sectors. Utilizing a mixed-methods approach, the research combines structured surveys of accounting professionals with in-depth interviews of stakeholders, including regulators and IT experts. Quantitative data were analyzed using descriptive statistics and regression models, while qualitative insights provided a deeper understanding of institutional readiness and implementation barriers. Results: The findings indicate that AI adoption in Zimbabwe is still in its early stages, with growing awareness of its benefits, such as automation and predictive analytics. However, challenges like limited digital infrastructure, high costs, skill shortages, and regulatory uncertainty impede widespread adoption. Conclusions: The study concludes that while AI holds transformative potential for financial reporting and auditing, a strategic and phased approach is crucial for successful integration. Limitations: Include a small sample size in certain sectors and reliance on self-reported data, which may introduce bias. Contributions: Despite these challenges, the research contributes significantly to the literature on AI in accounting in emerging economies, offering policy recommendations and practical frameworks to assist Zimbabwean institutions in leveraging AI for improved financial governance and oversight.